CIRCULAR MANUFACTURING OF THERMOELECTRIC MODULES SUPPORTED BY DQN DECISION-MAKING OPTIMIZATION
Published in GÉP 2024/2
https://doi.org/10.xxxx/GEP.2024.2.8
Albert Judit
PhD hallgató, Miskolci Egyetem, Gép- és Terméktervezési Intézet
Dr. Takács Ágnes
egyetemi docens, Miskolci Egyetem, Gép- és Terméktervezési Intézet
ABSTRACT
The optimization of thermoelectric modules brings significant benefits in terms of performance, weight, and cost. Optimization methods such as genetic algorithms, Deep Q-Learning, and the VIKOR method help finding the best compromise solutions. These optimized modules not only provide more efficient energy conversion but are also more economical and sustainable, contributing to long-term energy and environmental goals. Furthermore, the application of circular manufacturing principles minimizes waste and maximizes material recyclability, further enhancing sustainability
